Articles

The products of algorithmic and decision systems unleashed in the wild––put to use by governments, corporations, and civil society organizations––significantly impact how life happens and society functions. Recently, a Facebook whistleblower detailed how the inability to recognize these significant impacts, and the failure of federal lawmakers to protect people against them, has led to an exponential concentration of power.² Machine learning systems have produced biased outcomes in consequential areas like school admissions,³ government services,⁴ financial services,⁵ and healthcare.⁶ Algorithmic social media failures influence public opinion about governance, public health, and body image.⁷ Research demonstrates that distrust in automated systems is related to individual perceptions about whether a system can do what it promises to do.⁸ Therefore, when algorithms produce outcomes with tremendous, disparate, negative impacts, it results in calls for some kind of course correction.

Current such calls reflect the concern of many that there is a limited understanding of how a particular system works and comes to its decision. Demands for transparency and explainability in algorithmic systems are increasing, even spilling into the courts. In 2017, for example, the United States Supreme Court denied an appeal from a state supreme court hoping to force algorithmic transparency under constitutional grounds.⁹ In Loomis v. Wisconsin, a criminal defendant argued that the use of the COMPAS risk assessment tool at sentencing violated his right to due process “either because the proprietary nature of COMPAS prevents defendants from challenging the COMPAS assessment’s scientific validity, or because COMPAS assessments take gender into account.”¹⁰ The Wisconsin Supreme Court ultimately concluded that using the risk assessment tool for sentencing does not violate due process “if used properly, observing the limitations and cautions.”¹¹ This decision has been panned as a threat to constitutional due process.¹² It also illustrates the need for accountability for algorithms used in governance.¹³

Loomis reflects a concern about the black box nature of algorithmic tools and systems. Black boxes are those systems “colonized by the logic of secrecy,”¹⁴ having practices invisible to humans, and yet having significant impacts on our lives and the environments––legal, financial, social––that we inhabit. An “opening” of the black box requires more than just knowing the algorithmic processes, but understanding them as well, “doing neuroscience” as some have called it.¹⁵ Yet, legislators are considering how to require explainability for algorithmic systems; the right to explanation is included, for instance, in the EU’s General Data Protection Regulation (GDPR).¹⁶ Some argue, however, that explainability and transparency may be no match for algorithmic complexity.¹⁷

One way to understand complex ideas is through analogy. If, for example, algorithms are analogized to recipes,¹⁸ they assist in illustrating that these systems are programmed to use “raw” materials to produce other materials––decisions and/or predictions. Algorithms, as representation systems, provide “different ways of organizing, clustering, arranging and classifying concepts, and of establishing complex relations between them” and allowing for the production of correlations, or the formulation of (perceived) relationships.¹⁹ And although this analogy may not completely identify or allow for understanding the exact ways AI systems produce their decisions, it recognizes that these systems are based on data.²⁰ Because of this, a critical consideration of the data used to feed algorithms and how it is governed may be a way to circumvent the black box nature of these systems, and to obtain a better understanding of how they operate.

This essay offers a critical investigation of data and how it should be defined and governed to produce more transparency and mitigate possible harms to individuals and communities because of its use in AI systems. In essence, this essay argues that data should be viewed as a networked representation or observation. This definition recognizes that data is not singular, but always comes attached with labels, contexts, and biases fastened from its inception, if not collection, and that attachments increase depending on its place in the ecosystem. This view also requires a different strategy for governance––one that acknowledges data’s nature and networked existence, and moves beyond the individualistic, consent-based current models. Such an approach allows for the creation of better frameworks for collection, use, storage, access, and security of data. At the same time, this writing lays out a research agenda for further exploration of frameworks for harm reduction.

II. (Re)Defining Data

Data is governed based on how it is imagined and is defined. Property language and the rhetoric of ownership in relation to personal data, create a description of data divorced from individuals and ignore the potential for harm. Phrases like the colloquial “my data” or “their data,” depict data as though they were a singular, unattached object. The ongoing controversy of the increasing use of genetic ancestry databases by law enforcement may help to illustrate the potential for harm in this kind of understanding of data. Over the past few years, the popularity of DNA matching and ancestry information has grown, spurred, no doubt, by the speed of DNA sequencing and the relative inexpensiveness of home testing.²¹ This has meant the expansion of genetic testing outside of healthcare and paternity contexts, and into homes. Further, the popularity of ancestry-focused media content like, “Who Do You Think You Are?” and “Finding Your Roots” further drive interests in genetic testing and ancestry.²²

The commercial genetic testing industry in the United States has grown immensely, dominated by players like Ancestry.com, 23andMe, and GEDMatch. Individuals can purchase at-home testing kits that require them to send in a quantity of saliva or a cheek swab by mail. These are then tested for connections to samples already in the organization’s database, returning possible results about heritage, ethnic identity, and long-lost relatives. There is a genre of YouTube and other social media posts in which users reveal their genetic ancestry results, expressing ranges of emotions from surprise to anger to confusion.²³

While the popularity of direct to consumer DNA ancestry testing has been interesting for individuals, the possibilities of this database has not gone unnoticed by law enforcement officials.²⁴ In fact, over the past few years, the number of law enforcement requests for access to the data stored by these organizations has increased, no doubt spurred by the use by law enforcement in California to catch the famed Golden State Killer (GSK).²⁵ In that case, police requested access to the site GEDMatch to test against a sample of DNA left by the alleged killer at a crime scene. With access, law enforcement found someone who matched with a percentage of the police sample, indicating a “cousin” relationship.

The relative success of the GSK case has had reverberations both for law enforcement and the commercial DNA testing industry. First, law enforcement are now seeking further access to these databases.²⁶ This “crisis” has caused lawmakers in Utah to propose legislation that would deny law enforcement access to these databases.²⁷ Though such a proposal coming out of Utah may seem strange on its face, it is important to note that one of the largest commercial DNA testing organizations, Ancestry, is located in Utah and was founded by graduates of Brigham Young University.²⁸ For the DNA organizations themselves, the increase in law enforcement interest has required that they make critical decisions about access and searching of their sites, as well as terms of service for users. At the time of the GSK case, GEDMatch had a policy that allowed site users to opt-in to law enforcement searches.²⁹ After the GSK case the organization changed its terms of service to state, “We may disclose your Raw Data, personal information, and/or Genealogy Data if it is necessary to comply with a legal obligation such as a subpoena or warrant. We will attempt to alert you to this disclosure …unless notification is prohibited under law.” In 2019, GEDMatch was acquired by a forensic genomics firm that has stated that it cooperates with law enforcement.³⁰

The use by law enforcement of DNA databases without permission is already more than alarming. Advances in AI technology for use in analyzing the data stored in these systems is even more disturbing. Recently, for example, researchers have touted how advances in algorithmic technology could revolutionize DNA analysis for criminal investigations.³¹ Lacking, however, is a full consideration of what moving forward with such innovations could mean for civil rights, especially if mistakes are made in the data collection and aggregation processes.³² Instead, the focus has been on technology and how advances in technology might assist with data analysis. This may be a result of continued property-based language instead of language that more accurately reflects the nature of this kind of data.

Scholars have argued for other definitions of data that are useful for examining. One such definition is proposed by Ferryman under her conceptualization of data as gift as a framework for relationships related to data and participation.³³ Ferryman uses Marcel Mauss’ work on indigenous cultures and gift-giving as a framework for her definition. At the most basic level, Mauss found three elements for gifts: gift-giving, gift-receiving, and an obligation to reciprocate a gift.³⁴ From this Ferryman distills one principle: “there is no such thing as a free gift.” Therefore, data should be thought of as action that comes with the obligation for the individual or organization receiving it to reciprocate in some way. In the case of health and medical research, where data collection is integral to advances in treatment and diagnosis, it would place an obligation on the researchers to provide some kind of tangible benefit to the individuals, perhaps, in the form of relationships, making communities––particularly marginalized and vulnerable groups––into stakeholders instead of mere data subjects.

While the Ferryman’s concept of data as gift is meritorious and an important idea for building frameworks for interactions with communities, particularly in the public health context, it does not provide a true description of the thing at issue. Noted library and information scholar Christine Borgman defined data as: “Representations of observations, objects, or other entities used as evidence of phenomena for the purposes of research or scholarship.”³⁵ Borgman uses this concept of data in connection to an explication of data stewardship, particularly the ideal of FAIR: findable, accessible, interoperable, and reusable. Inherent in this definition of data is the emphasis on use for research or scholarship. If using a broad definition of research, this may work. However, this definition seems limited to a particular kind of use of data. It also misses the networked nature of data.

A broader and more complete definition of data is that of a networked representation or observation. This definition recognizes that data is not singular, but always comes attached with labels, contexts, and biases fastened from its inception, if not collection, and that attachments increase depending on its place in an ecosystem. An illustration from basic chemistry may be helpful. As noted above, data is usually conceptualized as a singular object, divorced from others. In that way, data is analogized to a solo atom. Yet, many of the elements on the periodic table of elements never appear as a single, solitary atom, but as molecules of more than one atom of the same nature.³⁶

A similar thing happens with data––there’s never just one datum collected, but several kinds of data. Even if a specific “data point” were examined, that particular point, too, would be endowed with other data that shape it, including the researcher’s choice in topic, participants, and research questions, among other things.³⁷ Likewise, an atom is made up of smaller molecular particles––protons, neutrons, and electrons––that shape its characteristics. An even more accurate definition of data, then, would be a system of networked representations or observations. This definition recognizes how data are used to make inferences, for evaluation, measurement, assessment, etc. of individuals, organizations, and programs. To do this, the data that represent must be arranged to decide or define relationships.

III. Why Data Governance?

A critique of data governance first requires an adequate definition of governance. Governance is more than government; it “refers to all processes of governing, whether undertaken by a government, market, or network, whether over a family, tribe, formal or informal organization, or territory, and whether through laws, norms, power, or language.”³⁸ Governance can be also described as “the mechanisms, processes and institutions, through which citizens and groups articulate their interests, exercise their legal rights, meet their obligations and mediate their differences.”³⁹ It is the word “process,” including social processes, that is of great importance, signifying that governance is more than just law; governance embodies both process and structure.⁴⁰

More than just the process, it is the process of decision-making that is the crux of governance. Decision-making requires the recognition and use of relationships, including the connections between actions and outcomes, as well as between products and services and organizations; it shapes the “rights, rules, preferences and resources that structure political outcomes.”⁴¹ A failure in governance is the use of law as a proxy for good decision-making, in place of recognizing the impacts of law on individuals and organizations. Therefore, data governance includes interventions aimed at “chang[ing..] data-related incentives, knowledge, institutions, decision-making, and behaviors.”⁴² More specifically, this definition encompasses the processes, decisions, and rules that organizations––governmental, civil society, and corporate––undertake when dealing with data. This also includes any partnership or so-called co-governance agreements between different organizations or networks of organizations.

Key to data governance is an understanding of what Sean McDonald calls the digital “supply chain”– “real-time network[s]” where organizations collect and move data through a system.⁴³ This means identifying the various organizations, motivations, and uses for data, as well as the possible conflicts and impact that may arise. The supply chain is integral to the data lifecycle, the stages of data from capture through interpretation and including storage. It is also important for understanding the possible impacts of data use. A recent report of the British Royal Society focused on good data governance across the lifecycle and identified issues that may arise with data that require governance infrastructure be set in place: data integrity, bias in data, accidental collection, crossing sectors, statistical profiling and stereotyping, transparency, accountability, and impact.⁴⁴ Current data governance regimes fail to meet both the requirements of good governance and to anticipate the issues that will emerge during the data lifecycle.

In the years since the coining of the phrase “big data,” there have been many guidelines, expressions of policy, and attempts at legislation to impede and/or mitigate the harms proffered by the collection of massive amounts of personal data. Technological advancements and the widespread centralization of personal data shared in networked platforms, too, have enticed legislators to attempt to do something about the possible harms. In the United States this has translated to the proposal and sometimes passage of bills that turn out to be sectoral, vague, underinclusive, impractical once passed, and/or so flexible so as to not really make much difference or cause added headaches for the people they were aimed at protecting.

While data governance includes legislation, failures in legislative execution demonstrate that laws are only part of the governance picture. Some attempts at data governance use a hybrid model––mixing several governance schemes in an attempt to achieve policy goals. An example of a kind of hybrid model and arguably ineffective governance scheme is used in the United States in the regulation of privacy and data protection, which sees an uncoordinated mix of state and federal agencies like the Federal Trade Commission and state attorneys general, as well as state and federal legislation and regulation. Tasked with protecting consumers from unfair and deceptive business claims and practices, the FTC’s power derives from federal legislation.⁴⁵ While not specifically mandated to work in the area of consumer privacy, this has come under its powers. The FTC has been involved with requiring business transparency about privacy with privacy policy regulations for example. At the same time, and under the same privacy threats, the FTC may not take on all data practices that evoke individual privacy; the Agency would need a significant increase in funding to do so. Further, under many of the privacy “regulations,” individuals have no privacy right of action but must wait on the FTC to enforce the law’s prohibitions. This is a concentration of power in a particular agency. This does not mean that individuals have no recourse against organizations. Of course, state and other laws may allow civil suits or state attorneys general to pursue criminal penalties. It does demonstrate how hybrid systems, without coordination of organizations and some form of omnibus structure and decision-making and foundation, can limit data protections.

Data protection is a significant concern in emerging scholarship and policy on platform governance. Although not the focus of this article, it is important to mention platform governance, which has emerged in response to various scandals and revelations about media and technology that have become integral and ubiquitous. Platform governance is a frame that recognizes that “platforms are fundamentally political actors that make important political decisions while engineering what has become the global infrastructure of free expression,”⁴⁶ while at the same time recognizing that organizations are subject to external governance. This then requires the identification of the various actors involved in platform governance including the usual suspects of government, users, and the platforms themselves, but also including related organizations like advertisers, data-brokers, and “other parties that participate in the platform’s ecosystem.”⁴⁷

In fact, “platform-driven ecosystems” that allow multiple actors to participate have been called the “future of the digital age.”⁴⁸ Platforms—organizations that “leverage networked technologies to facilitate economic exchange, transfer information, connect people, and make predictions,”⁴⁹ continue to emerge as the business model of choice for organizations across several industries. Platforms and the ecosystems that emerge surrounding them, are, in fact governance networks that take, for the most part, the form of a hybrid governance network: having the participants in the network engaged with a central organization (the platform organization itself). Unlike the lead-organization governed network identified by Provan and Kenis,⁵⁰ in platform ecosystems power is concentrated in the lead organization, which governs⁵¹ through various agreements, policies, and contracts with other actors in the system. Platforms are significant for data collection, use, security, etc. It is important, then, to comprehend the roles they play and their relationships to data and other actors and actants in the data ecosystem.

IV. An Ecological Approach to Data and Governance

Platforms provide infrastructure for parts of the data ecosystem. The study of ecology, usually considered under the umbrella of biology or biological science, is the study of systems and structures. That is, the field of ecology concerns itself with not only a specific item, say a human or badger. Instead, ecology examines the relationships between the item and other items and systems in its physical environment.⁵² Human ecology, in particular, centers humans as the organism of interest, with human ecology theory finding the significance in studying both the human and their social interactions.⁵³

In human development, ecological systems theory details how a person’s immediate environment along with “social context, both formal and informal” in which the environments rested influenced the process of human development.⁵⁴ Bronfenbrenner proposed a change in the traditional method of considering human development, which focused either on naturalistic observations of humans at particular points of development, often considering only one “being” at a time and in one setting. He argued that true understanding of human development “requires examination of multiperson systems of interaction not limited to a single setting and must take into account aspects of the environment beyond the immediate situation containing the subject,”⁵⁵ which required envisioning the “environment” for a human as a model of four nested systems: micro, meso, exo, and macro. In brief, the microsystem includes the direct subject of study. In human development, this would be the human. The microsystem rests within the mesosystem, which contains that human’s relationships or connections. The exosystem, which encompasses the mesosystem, includes all of the formal and informal structures that influence human development. Finally, the macrosystem represents the various environments in which the human, their relationships, and structures inhabit, including the social, political, economic, and legal, among others. According to Bronfenbrenner, this kind of ecological model represents the complexity of human development and ecology, taking into account the various things that shape who a person is and becomes.⁵⁶

This kind of ecological thinking––considering relationships and connections between things––has been used outside of the physical sciences in social sciences like psychology, mass communication, education, and social work. In the scholarship on community health interventions in particular, the approach is to consider several layers of systems to understand and shape specific outcomes for those living within a community. Stokols chronicles the social ecological approach used for studying community health campaigns.⁵⁷ This set of principles offers a “framework for understanding the dynamic interplay among persons, groups, and their sociophysical milieus.”⁵⁸ Within this context, the paradigm recognizes the physical, social, and cultural dimensions to health, and incorporates terminology from ecology such as interdependence, negative feedback, and amplification, among others. Ultimately, this framework is interdisciplinary.

Likewise, in communication research, scholars have taken ecological approaches to the study of journalism and other media structures and their influence on humans and human behavior. An example of mass communication research using an ecological approach to studying media is communication infrastructure theory. Promulgated by Sandra Ball-Rokeach and several of her colleagues, communication infrastructure theory deems storytelling networks as essential for the development and sustainment of civic engagement.⁵⁹ In particular, the theory argues that communication resources enable individuals to engage in collective action, and that “storytelling networks” set in a communication action context describes the nexus of interpersonal, organizational, and institutional communication relationships that assist in cultivating neighborhood belonging, which can lead to civic engagement. CIT, then, requires a consideration of three different kinds of storytelling agents: micro––the residents of a community; meso––the specific neighborhood; and macro––the entire community.⁶⁰ The aim of using CIT as a framework is to assist with understanding the motivations for specific kinds of civic engagement and participation, as well as identifying systems and structures that influence participation. Of course, an environmental or ecology-related approach is not new for law and policy particularly as it relates to information. In discussions of policy and the public domain, scholars have considered the analogies of environmentalism⁶¹ and the use of raw materials.⁶²

An ecological approach for considering how data should be governed is appropriate because it assists with identifying the specific thing to be governed, that thing’s relationships/connections that can and/or should influence govern choices, the institutions and societal structures that impact govern and who will be tasked with enforcement and implementation, and the environment(s) in which data governance must occur. All of these many factors must be examined to achieve anywhere near a comprehensive and adequate response to the massive volume of data collection, continued surveillance, and data misuse. The next section details the four nested systems of data governance ecology in this approach, providing descriptions of ongoing genetic databases conflicts to help illustrate the importance of considering the various levels of the model.

A. Microsystem–data representations

Like with Bronfenbrenner’s original explication of the ecological approach to human development, this ecological approach to data governance begins with the microsystem, encompassing data, defined above as networked representations. This means that within this system is the foundation layer of the data itself. Understanding the microsystem essential for good ecological governance because it identifies the two functions of data: as thing and as action. Data as thing applies Buckland’s conceptualization of “information as thing”––”objects . . . that are regarded as being informative”⁶³ to data, making it of interest to research systems. Data as a thing allows organizations to make inferences. As the volume of data increases, uncertainty and equivocality decrease for organizations. Therefore, data as thing can be viewed as evidence, “though without implying that [the evidence is] necessarily accurate, useful, or even pertinent to the user’s purposes.”⁶⁴ Important in this idea of data-as-thing-as-evidence is the implication that the “thing” is passive – it does nothing, but something is done to it or with it.

In contrast, data as action data as process; it changes what organizations know; it informs. Data as action also harkens to Ferryman’s definition of data as requiring reciprocity. In the case of organizations collecting data, this will mean that the act of data collection initiates certain duties.⁶⁵ Future research must consider both data as thing and data as action to inform about how they behave in the data ecosystem, and their relations.

B. Meso–data relationships

Surrounding the microsystem is the mesosystem, which considers the relationships and connections to data. If our definition of data is that of a networked representation. It is important to consider what things are in the network. Bronfenbrenner describes the mesosystem as a system of microsystems. For data, this would mean examining both the connections that data has with other kinds of data as well as the attachments and labels connected with the data. The mesosystem also encompasses all of the structures or settings that shape data over the life cycle.

In the GEDMatch DNA case in which the Golden State Killer was identified through law enforcement use of a commercial DNA database, understanding the nature of the various relationships that were implicated in a DNA search may have at least given state legislators pause about the kinds of laws necessary to ensure user privacy. On the federal level, of course, the Genetic Information Non-discrimination Act (GINA) exists to prohibit discrimination based on genetic data. But GINA is narrowly focused on discrimination and only applies to the healthcare and employment sectors. Several state laws focused on genetic non-discrimination also exist; these too narrowly focused on insurance and employment. This fails to prevent use of genetic data for purposed beyond particular expectations and ignores the relational harms that can be caused by misuse of the data. Future research, then, must identify and investigate the influence of these data relationships, in order to create adequate policy for governance.

C. Exo–institutions/community

The exosystem includes all of the societal and institutional structures that mediate data and data governance. These structures will be tasked with implementing and enforcing data governance, but also implicate how they use data, which leads to the need for stronger data governance regulation. This system also identifies the kinds of infrastructure necessary for providing adequate data governance and embodies all of the formal and informal social structures that influence the data lifecycle. This includes all of the major societal institutions, technology, and platforms, as well as governmental and civil society organizations.⁶⁶ That data encounters over the lifecycle or that shapes how data moves through the lifecycle.

At the same time, an exosystem “has been defined as consisting of one or more settings that do not involve … an active participant but in which events occur that affect, or are affected by, what happens in that setting.”⁶⁷ Therefore, exosystems concern settings and actors that indirectly affect data. It is important to investigate the structures in the exosystem as though they may not have direct connections to individuals, they nonetheless may impact how data is collected, used, etc. As an example, many social media sites, employment databases for various public professions, as well as law enforcement make photos available online. These photos have become the fuel for organizations developing facial recognition systems that scrape social media and other databases to train their software. An ecological approach would examine these facial recognition platforms, their attending organizations, as well as the spaces where they collect data. It will also be important to understand the law, or lack thereof, as boundary infrastructure in this line of research.

D. Macro–environment(s) i.e., social, political, economic, etc.

The macrosystem is the environment(s) in which the micro, meso, and exo systems rest. Under Bronfenbrenner’s conceptualization, the macrosystem “refers not to specific contexts … but to general prototypes, existing in the culture or subculture, that set the pattern for the structures and activities occurring at the concrete level.”⁶⁸ These prototypes are called the “blueprints” for society because they hold true for both formal and informal settings⁶⁹. Culture is expressed in law, customs, belief systems, economic structures, etc. It is important for future research to examine how these prototypes shape all of the other systems. For data, this would mean thorough investigations of how culture and subcultures shape the environment for data and are then expressed in how various organizations and individuals respond.

At the same time, it is important to consider how culture actually behaves. A criticism of Bronfenbrenner’s theory is that it relegates culture to the macrosystem, as though culture does not permeate all systems in human ecology.⁷⁰ Culture permeates everything, structure, and setting in society. Therefore, ignoring culture presents a view of data and the institutions and organizations connected with data as neutral, in spite of the overwhelming evidence to the contrary.⁷¹ This requires a reconsideration of how the macrosystem operates. While traditionally viewed as the most outer oval of the model, or the largest nesting doll, it would be more accurate to view the macrosystem as closer to atmospheric, flowing through all other systems and circulating throughout the various systems, closely related to Appadurai’s conception of scapes that influence information flows and denote the fluidity of five dimensions of cultural flows.⁷² Though Appadurai applied this suffix to characteristics in relation to the international capital, it works with data, which too is subject to international flows.

V. Data Ecology in Action

Ecology recognizes that things within a system, or a system of systems, impact and/or change other things. But ecology and ecosystems are not clean models; they are messy.

This is an actual ecosystem – containing representations (data) that encounter other data, and communities, where institutions reside that enforce and enable flows, all embedded within economic, legal, and social, among other, environments. The messiness of this system is the point. A simple analogy, like that of property, does not work for a system like data where humans are involved.

A recent controversy with the U.S. National Institutes of Health All of Us precision medicine initiative illustrates the necessity of considering an ecological approach to data governance. All of Us is a research program that aims to recruit one million people in the U.S. from which to gather health data and specimens.⁷³ This information will be used for biomedical research and includes “health questionnaires, electronic health records (EHRs), physical measurements, the use of digital health technology, and the collection and analysis of biospecimens.”⁷⁴ The system is also billed as allowing researchers “researchers to take into account individual differences in lifestyle, socioeconomic factors, environment, and biologic characteristics in order to advance precision diagnosis, prevention, and treatment.”⁷⁵

But the breadth of this research data and the possible inferences that can be made from it are of concern for several tribal communities. In the U.S., there are nearly 600 federally recognized Tribal governments, which exercise sovereignty over many vectors of tribal life including public health data.⁷⁶ In 2018, however, it was reported that the NIH was bypassing tribal data sovereignty to collect the data by recruiting in urban areas containing large populations of Native Americans, without consulting with tribes or the National Congress of American Indians.⁷⁷ At issue is the sharing of EHRs and other data with pharmaceutical and other organizations. Further, a question remains about the applicability of the Health Insurance Portability and Accountability Act (“HIPAA”) to the organizations would be able to access the information.⁷⁸

For some, the actions of the NIH and other researchers and programs who have sought indigenous data is a form of biocolonialism––the assertion of control, ownership, and use of biological data and specimens without or beyond the guidance of tribal governments and without direct benefit.⁷⁹ The result has been a call for both decolonizing data⁸⁰ and for the recognition of indigenous data sovereignty.⁸¹ The move for indigenous data sovereignty has been long, but the first recognized formal international convening happened in 2015, with a meeting of indigenous researchers in Australia.⁸² Following this, collectives of indigenous formed groups and established charters aimed at creating guidance for data sovereignty. In the U.S. one such group is the US Indigenous Data Sovereignty Network (“USIDSN”) that aims to “promot[e] Indigenous data sovereignty through decolonizing data and Indigenous data governance.”⁸³ For collectives like USIDSN the principles of data sovereignty reflect a different framework than that traditionally used in the Western governance. Sovereignty, under tribal governance may take several forms,⁸⁴ but offers a way forward for tribes with the aim of protecting privacy, preempting extractive research, and recognizing the implications of data use on the many interconnected facets of life for Native Americans.⁸⁵

The All of Us controversy demonstrates both the need for more adequate data governance that recognizes the implications of the data ecosystem, as well as the need for action ensuring that the awareness of these systems is included in the development of its frameworks. Good governance, in general, is collective, responsive, equitable, and lawful. In studying and enacting ecological data governance, we must use collective and participatory approaches to the creation of frameworks. This requires engagement with traditionally marginalized and vulnerable communities, many of whom are disparately impacted by data collection and uses. It also demands that organizations––whether civic, civil society, or corporate–– be responsive to collective governance decisions. Accountability necessitates legislation as an encouragement. Legislation also acts as infrastructure, and good data governance requires infrastructure, which will include platforms and mechanisms that perform the frameworks produced.

This essay has sought to provide a brief overview of a way forward for considering and governing the materials that feed the ever-burgeoning AI technological ecosystem. It further provides a research agenda for exploring exactly how this framework could work while focusing on the various aspects of the data governance scheme. It will be ever more important to investigate methods of harm reduction as the use of algorithmic systems expands.

* Associate Professor, University of Florida. Much appreciation goes to the helpful reviewers and participants at the Privacy Law Scholars Conference. ↩︎
See John D. McKinnon & Ryan Tracy, Facebook Whistleblower’s Testimony Builds Momentum for Tougher Tech Laws, Wall St. J., https://www.wsj.com/articles/facebook-whistleblower-frances-haugen-set-to-appear-before-senate-panel-11633426201, (Oct. 5, 2021, 5:21 PM). ↩︎
See D. J. Pangburn, Schools Are Using Software to Help Pick Who Gets In. What Could Go Wrong?, Fast Co. (May 17, 2019), https://www.fastcompany.com/90342596/schools-are-quietly-turning-to-ai-to-help-pick-who-gets-in-what-could-go-wrong; Oscar Schwartz, Untold History of AI: Algorithmic Bias Was Born in the 1980s, IEEE Spectrum (Apr. 15, 2019), https://spectrum.ieee.org/tech-talk/tech-history/dawn-of-electronics/untold-history-of-ai-the-birth-of-machine-bias. ↩︎
See Faith Gordon, Book Review, 1 L. Tech. Humans 162, 162 (2019) (reviewing Virginia Eubanks, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (2018)), Rebecca Heilweil, Why Algorithms Can Be Racist and Sexist, Vox (Feb. 18, 2020, 12:20 PM), https://www.vox.com/recode/2020/2/18/21121286/algorithms-bias-discrimination-facial-recognition-transparency. ↩︎
See Jennifer Miller, Is an Algorithm Less Racist Than a Loan Officer?, N.Y. Times (Sep. 18, 2020), https://www.nytimes.com/2020/09/18/business/digital-mortgages.html; Michelle Seng Ah Lee & Luciano Floridi, Algorithmic Fairness in Mortgage Lending: From Absolute Conditions to Relational Trade-offs, 31 Minds & Machs. 165 (2021). ↩︎
See Heidi Ledford, Millions Affected by Racial Bias in Health-Care Algorithm, 574 Nature 608 (2019); Eliza Strickland, Health Care Algorithms Show Racial Bias, IEEE Spectrum, Jan. 2020, at 6 (2020). ↩︎
See McKinnon & Tracy, supra note 1. ↩︎
See Mary T. Dzindolet et al., The Role of Trust in Automation Reliance, 58 Int’l J. Hum.-Comput. Stud. 697 (2003); Jiun-Yin Jian et al., Foundations for an Empirically Determined Scale of Trust in Automated Systems, 4 Int’l J. Cognitive Ergonomics 53 (2000); John O’Donovan & Barry Smyth, Trust in Recommender Systems, in IUI ‘05: Procs. of the 10th Int’l Conf. on Intelligent User Interfaces 167 (2005), http://portal.acm.org/citation.cfm?doid=1040830.1040870. ↩︎
Loomis v. Wisconsin, 137 S. Ct 2290 (2017) (cert. denied). ↩︎
State v. Loomis, 881 N.W. 2d 749, 753 (Wis. 2016). ↩︎
Id. at 753. ↩︎
See Katherine Freeman, Algorithmic Injustice: How the Wisconsin Supreme Court Failed to Protect Due Process Rights in State v. Loomis, 18 N.C. J. L. & Tech. 75 (2016); John Lightbourne, Damned Lies & Criminal Sentencing Using Evidence-Based Tools, 15 Duke L. & Tech. Rev. 327 (2017). ↩︎
See Han-Wei Liu et al., Beyond State v. Loomis: Artificial Intelligence, Government Algorithmization, and Accountability, 27 Int’l J. L. & Info. Tech. 122 (2019). ↩︎
Frank Pasquale, The Black Box Society 2 (2015). ↩︎
Davide Castelvecchi, The Black Box of AI, 538 Nature 20 (2016). ↩︎
But see Sandra Wachter et al., Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR, 31 Harv. J. L. & Tech. 841 (2018); Sandra Wachter et al., Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation, 7 Int’l Data Priv. L. 76 (2017) (arguing that the GDPR does not actually create an explainability requirement).
↩︎
See Yavar Bathaee, The Artificial Intelligence Black Box and the Failure of Intent and Causation, 31 Harv. J. L. & Tech. 889, 891 (2017). ↩︎
See Kevin D. Ashley & Edwina L. Rissland, Law, Learning and Representation, 150 A.I. 17 (2003); M. Galieh Gunagama, Generative Algorithms in Alternative Design Exploration, SHS Web Conf., vol. 41 2018, at 2. ↩︎
Stuart Hall, Representation: Cultural Representation and Signifying Practices 15, 17 (1997). ↩︎
See Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (2016); Gordon, supra note 3. ↩︎
See Jennifer King, “Becoming Part of Something Bigger”: Direct to Consumer Genetic Testing, Privacy, and Personal Disclosure, Proc. ACM Hum.-Computing Interaction, Nov. 2019, at 158:1; Antonio Regalado, 2017 Was the Year Consumer DNA Testing Blew Up, MIT Tech. Rev. (Feb. 12, 2018), https://www.technologyreview.com/2018/02/12/145676/2017-was-the-year-consumer-dna-testing-blew-up/. ↩︎
See Ashley Barnwell, The Genealogy Craze: Authoring an Authentic Identity through Family History Research, 10 Life Writing 261, 262 (2013); Wendy D. Roth & Biorn Ivemark, Genetic Options: The Impact of Genetic Ancestry Testing on Consumers’ Racial and Ethnic Identities, 124 Am. J. Socio. 150 (2018). ↩︎
See Anna Harris et al., Autobiologies on YouTube: Narratives of Direct-to-Consumer Genetic Testing, 33 New Genetics & Soc’y 60 (2014). ↩︎
See Claire Abrahamson, Guilt by Genetic Association: The Fourth Amendment and the Search of Private Genetic Databases by Law Enforcement, 87 Fordham L. Rev. 50 (2019); Christi J. Guerrini et al., Should Police Have Access to Genetic Genealogy Databases? Capturing the Golden State Killer and Other Criminals Using a Controversial New Forensic Technique, PLOS. Biol., Oct. 2018, at 1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6168121/; Rachele M. Hendricks-Sturrup et al., Direct-to-Consumer Genetic Testing and Potential Loopholes in Protecting Consumer Privacy and Nondiscrimination, 321 J. Am. Med. Assoc. 1869 (2019); Joseph Zabel, The Killer Inside Us: Law, Ethics, and the Forensic Use of Family Genetics, 24 U.C. Berkeley J. Crim. L. 47 (2019). ↩︎
See Claire Abrahamson, Guilt by Genetic Association: The Fourth Amendment and the Search of Private Genetic Databases by Law Enforcement, 87 Fordham L. Rev. 50 (2019); Christi J. Guerrini et al., Should Police Have Access to Genetic Genealogy Databases? Capturing the Golden State Killer and Other Criminals Using a Controversial New Forensic Technique, PLOS. Biol., Oct. 2018, at 1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6168121/; Rachele M. Hendricks-Sturrup et al., Direct-to-Consumer Genetic Testing and Potential Loopholes in Protecting Consumer Privacy and Nondiscrimination, 321 J. Am. Med. Assoc. 1869 (2019); Joseph Zabel, The Killer Inside Us: Law, Ethics, and the Forensic Use of Family Genetics, 24 U.C. Berkeley J. Crim. L. 47 (2019). ↩︎
See Megan Molteni, The Creepy Genetics Behind the Golden State Killer Case, Wired (Apr. 27, 2018, 2:00 PM), https://www.wired.com/story/detectives-cracked-the-golden-state-killer-case-using-genetics/; Heather Murphy, She Helped Crack the Golden State Killer Case. Here’s What She’s Going to Do Next., N.Y. Times (Aug. 29, 2018), https://www.nytimes.com/2018/08/29/science/barbara-rae-venter-gsk.html; Sarah Zhang, How a Genealogy Website Led to the Alleged Golden State Killer, Atlantic (Apr. 27, 2018), https://www.theatlantic.com/science/archive/2018/04/golden-state-killer-east-area-rapist-dna-genealogy/559070/. ↩︎
See Emma Coleman, One State May Become the First to Ban Law Enforcement Use of Genealogy Databases, Route Fifty (Jan. 21, 2020), https://www.routefifty.com/public-safety/2020/01/utah-dna-databases/162544/. ↩︎
See Jennifer Graham, The Company That Analyzed Your DNA Just Sold the Results to Someone Else. Really, What Are the Risks?, Deseret News (Aug. 21, 2018, 2:26 PM), https://www.deseret.com/2018/8/21/20651592/the-company-that-analyzed-your-dna-just-sold-the-results-to-someone-else-really-what-are-the-risks; Stuart Leavenworth, DNA for Sale: Ancestry Wants Your Spit, Your DNA and Your Trust. Should You Give Them All 3?, Tampa Bay Times (June 3, 2018), https://tampabay.com/news/business/DNA-for-Sale-Ancestry-wants-your-spit-your-DNA-and-your-trust-Should-you-give-them-all-3-_168819151/. ↩︎
See Nila Bala, We’re Entering a New Phase in Law Enforcement’s Use of Consumer Genetic Data, Slate (Dec. 19, 2019, 7:30 AM), https://slate.com/technology/2019/12/gedmatch-verogen-genetic-genealogy-law-enforcement.html; Natalie Ram, The Genealogy Site That Helped Catch the Golden State Killer Is Grappling With Privacy, Slate (May 29, 2019, 7:30 AM), https://slate.com/technology/2019/05/gedmatch-dna-privacy-update-law-enforcement-genetic-geneology-searches.html. ↩︎
Bala, supra note 28. ↩︎
See Karen Richmond, AI Could Revolutionise DNA Evidence – But Right Now We Can’t Trust the Machines, The Conversation (Jan. 29, 2020, 6:35 AM), http://theconversation.com/ai-could-revolutionise-dna-evidence-but-right-now-we-cant-trust-the-machines-129927; Chris Baraniuk, The New Weapon in the Fight Against Crime, BBC (Mar. 3, 2019), https://www.bbc.com/future/article/20190228-how-ai-is-helping-to-fight-crime. ↩︎
See Rashida Richardson et al., Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice, 94 N.Y.U. L. Rev. Online 15, 42 (2019), for a discussion of issues with dirty data. ↩︎
Kadija Ferryman, Reframing Data as a Gift (Apr. 17, 2017) (unpublished remarks), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3000631. ↩︎
Marcel Mauss, The Gift: The Form and Reason for Exchange in Archaic Societies (2002). ↩︎
Christine L. Borgman, presentation to the National Press Club: Unstable in Concept and Context (Nov. 15, 2019), https://escholarship.org/uc/item/0zf478ch. ↩︎
David W. Ball & Jessie A. Key, Molecules and Chemical Nomenclature, in Introductory Chemistry – 1st Canadian Edition (2014). ↩︎
See Richard A. Berk, An Introduction to Sample Selection Bias in Sociological Data, 48 Am. Socio. Rev. 386 (1983); Jelke Bethlehem, Selection Bias in Web Surveys, 78 Int’l Stat. Rev. 161 (2010); M. Delgado-Rodríguez & J. Llorca, Bias, 58 J. Epidemiol Cmty. Health 635 (2004). ↩︎
Mark Bevir, Governance: A Very Short Introduction 1 (2012). ↩︎
UNDESA, UNDP & UNESCO, UN System Task Team on the Post-2015 UN Development Agenda: Governance and Development (May 2012), https://www.un.org/millenniumgoals/pdf/Think%20Pieces/7_governance.pdf. ↩︎
Peter Bogason & Juliet A. Musso, The Democratic Prospects of Network Governance, 36 Am. Rev. Pub. Admin. 3 (2006). ↩︎
James G March & Johan P Olsen, Democratic Governance (1995). ↩︎
Maria Carmen Lemos & Arun Agrawal, Environmental Governance, 31 Ann. Rev. Env’t. Res. 297, 298 (2006). ↩︎
Sean Martin McDonald, From Space to Supply Chain: Humanitarian Data Governance (Aug. 12, 2019), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3436179. ↩︎
The Royal Society, Connecting Debates on the Governance of Data and Its Uses (July 16, 2016), http://www.webscience.org/wp-content/uploads/sites/117/2016/12/DES4610_Data-Governance-report.pdf. ↩︎
Woodrow Hartzog & Daniel J. Solove, The Scope and Potential of FTC Data Protection, 83 Geo. Wash. L. Rev. 2230 (2015); Daniel J. Solove & Woodrow Hartzog, The FTC and the New Common Law of Privacy, 114 Colum. L. Rev. 583 (2014). ↩︎
Robert Gorwa, What is Platform Governance?, 22 Info. Commc’n Soc’y 854, 857 (2019). ↩︎
Id. ↩︎
Mark Fenwick et al., The End of ‘Corporate’ Governance: Hello ‘Platform’ Governance, 20 Eur. Bus. Org. L. Rev. 171, 177 (2019). ↩︎
Id. at 171. ↩︎
Keith G. Provan & Patrick Kenis, Modes of Network Governance: Structure, Management, and Effectiveness, 18 J. Pub. Admin. Rsch. Theory 229 (2007). ↩︎
Kate Klonick, Does Facebook’s Oversight Board Finally Solve the Problem of Online Speech?, in Models for Platform Governance 51–53 (2019). ↩︎
Sean Esbjörn-Hargens & Michael E. Zimmerman, An Overview of Integral Ecology 9 (Integral Institute, Resour. Pap. No. 2, 2009); David B. Lindenmayer et al., Value of Long-term Ecological Studies, 37 Austral Ecology 745 (2012). ↩︎
Margaret M. Bubolz & M. Suzanne Sontag, Human Ecology Theory, in Sourcebook of Family Theories and Methods: A Contextual Approach 419–50 (Pauline Boss et al. eds., 1993), https://doi.org/10.1007/978-0-387-85764-0_17. ↩︎
Urie Bronfenbrenner, Toward an Experimental Ecology of Human Development., 32 Am. Psych. 513 (1977). ↩︎
Id. at 514. ↩︎
Id. at 515. ↩︎
D. S. Stokols, Translating Social Ecological Theory Into Guidelines for Community Health Promotion, 10 Am. J. Health Promotion 282 (1996), https://escholarship.org/uc/item/2bv79313. ↩︎
Id. at 283. ↩︎
See Garrett M. Broad et al., Understanding Communication Ecologies to Bridge Communication Research and Community Action, 41 J. Applied Commc’n Rsch. 325, 325–45 (2013); see also Yong-Chan Kim & Sandra J. Ball-Rokeach, Community Storytelling Network, Neighborhood Context, and Civic Engagement: A Multilevel Approach, 32 Hum. Commc’n Rsch. 411, 411–39 (2006); Yong-Chan Kim & Sandra J. Ball-Rokeach, Civic Engagement from a Communication Infrastructure Perspective, 16 Commc’n Theory 173, 173–97 (2006); Holley A. Wilkin et al., Applications of Communication Infrastructure Theory, 25 Health Commc’n 611, 611–12 (2010). ↩︎
See Kim & Ball-Rokeach, supra note 58. ↩︎
See James Boyle, A Politics of Intellectual Property: Environmentalism for the Net?, 47 Duke L.J. 87 (1998); Anupam Chander & Madhavi Sunder, The Romance of the Public Domain, 92 Calif. L. Rev. 1331, 1331–74 (2004). ↩︎
See Julie E. Cohen, The Biopolitical Public Domain: The Legal Construction of the Surveillance Economy, 31 Phil. Tech. 213, 213–33 (2018). ↩︎
Michael K. Buckland, Information as Thing, 42 J. Am. Soc’y Info. Sci. 351, 351 (1991). ↩︎
Id. at 353. ↩︎
See Jack M. Balkin, Information Fiduciaries and the First Amendment, 49 U.C. Davis L. Rev. 1183–34 (2016); Ariel Dobkin, Information Fiduciaries in Practice: Data Privacy and User Expectations, 33 Berkeley Tech. L. J. 1, 1–50 (2018); Jonathan Zittrain, Engineering an Election, 127 Harv. L. Rev. F. 335, 335–41 (2014). ↩︎
See Bronfenbrenner, supra note 53, at 515. ↩︎
Urie Bronfenbrenner, The Ecology of Human Development 237 (1979). ↩︎
See Bronfenbrenner, supra note 53, at 515. ↩︎
See Nicole M. Vélez-Agosto et al., Bronfenbrenner’s Bioecological Theory Revision: Moving Culture from the Macro into the Micro, 12 Persps. Psych. Sci. 900, 902 (2017). ↩︎
See id. at 906. ↩︎
See Eubanks, supra note 3; Safiya Umoja Noble, Algorithms of Oppression: How Search Engines Reinforce Racism (2018); see generally O’Neil, supra note 19. ↩︎
See Arjun Appadurai, Disjuncture and Difference in the Global Cultural Economy, 7 Theory Culture & Soc’y 295, 295–310 (1990). ↩︎
See The “All of Us” Research Program, 381 New Eng. J. Med. 668, 668 (2019); see also National Institutes of Health (NIH)—All of Us, Nat’l Inst. of Health (NIH) (2020), https://allofus.nih.gov/future-health-begins-all-us (last visited Feb 20, 2021). ↩︎
The “All of Us” Research Program, supra note 72. ↩︎
Id. ↩︎
See Aila Hoss, Exploring Legal Issues in Tribal Public Health Data and Surveillance, 44 S. Ill. U. L.J. 27, 27 (2019). ↩︎
See Terri Hansen & Jacqueline Keeler, The NIH Is Bypassing Tribal Sovereignty to Harvest Genetic Data from Native Americans, Vice, Dec. 21, 2018, https://www.vice.com/en/article/8xp33a/the-nih-is-bypassing-tribal-sovereignty-to-harvest-genetic-data-from-native-americans; Kalen Goodluck, Indigenous Data Sovereignty Shakes Up Research, High Country News, Oct. 8, 2020, https://www.hcn.org/issues/52.11/indigenous-affairs-covid19-indigenous-data-sovereignty-shakes-up-research. ↩︎
See Hansen & Keeler, supra note 76. ↩︎
See Manola Secaira, Abigail Echo-Hawk on the Art and Science of ‘Decolonizing Data,’ Crosscut, May 31, 2019, https://crosscut.com/2019/05/abigail-echo-hawk-art-and-science-decolonizing-data; see also Hansen & Keeler, supra note 76; Kalen Goodluck, Covid is Strengthening the Push for Indigenous Data Control, Wired, Oct. 10, 2020, https://www.wired.com/story/covid-is-strengthening-the-push-for-indigenous-data-control/. ↩︎
See Secaira, supra note 78. ↩︎
See Goodluck, supra note 76. ↩︎
See Te Mana Raraunga – Māori Data Sovereignty Network Charter (2015), Māori Data Sovereignty Network, https://static1.squarespace.com/static/58e9b10f9de4bb8d1fb5ebbc/t/5913020d15cf7dde1df34482/1494417935052/Te+Mana+Raraunga+Charter+%28Final+%26+Approved%29.pdf (last visited Feb. 20, 2021). ↩︎
United States Indigenous Data Sovereignty Network, About, https://web.archive.org/web/20220423084152/https://usindigenousdata.org/about-us (last visited Feb 20, 2021). ↩︎
See Krystal S. Tsosie, Models of Data Governance and Advancing Indigenous Genomic Data Sovereignty, in Proceedings of the 26^th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 3592 (2020), https://doi.org/10.1145/3394486.3411072 (last visited Feb. 20, 2021). ↩︎
See Rebecca Tsosie, Tribal Data Governance and Informational Privacy: Constructing “Indigenous Data Sovereignty,” 80 Mont. L. Rev. 230, 231 (2019); See also Hoss, supra note 75, at 28. ↩︎

Articles

Law School Rankings And Political Ideology: Measuring The Conservative Penalty And Liberal Bonus With Updated 2023 Rankings Data

December 20, 2023December 20, 2023 JLEPP

[PDF]

Michael Conklin¹^*

Introduction

In 2020, novel research was conducted to measure whether, and to what extent, conservative law schools are punished and liberal law schools are rewarded in the U.S. News & World Report peer rankings.² The study found a drastic conservative penalty and liberal bonus that amounted to a difference in the peer rankings of twenty-eight spots.³ This Article updates the research using the latest political affiliation data and the most recent 2023 rankings data. The updated results produce an astounding thirty-two-place difference in the peer rankings attributable to political ideology. This increase from the 2020 research elicits discussion regarding the effects of recent societal changes in polarization and civility. This Article discusses how this disparity in the rankings may perpetuate a lack of ideological diversity in legal academia. The harm to professors, students, and society at large from such a lack of ideological diversity in law schools is discussed. Finally, this Article concludes by proposing a simple solution to circumvent this particular manifestation of ideological bias in legal academia.

I. Law School Rankings

The U.S. News & World Report overall rankings (hereinafter “overall rankings”) are based primarily on objective data, such as bar passage rate, employment rate, Law School Admission Test (LSAT) score, undergraduate grade point average (GPA), acceptance rate, and student–faculty ratio.⁴ They are also the standard for measuring American law school prestige.⁵ Law schools have responded accordingly by altering their behavior in attempts to improve their rankings.⁶ The incentive to improve one’s ranking is so strong that some law schools even go so far as to falsely report data⁷ and coerce underachieving graduates to delay taking the bar exam.⁸ Undesirable law school ratings frequently result in the firing of deans.⁹ Even the perceived value of a law journal is affected by that school’s ranking.¹⁰

The U.S. News & World Report also provides peer rankings, which are the sole result of surveys completed by law school deans and select faculty regarding their perceptions of law schools.¹¹ The existence of these two rankings—one mostly objective and one entirely subjective—allows for analysis on which schools have disproportionately high or low reputations based on what would be expected from their objective performances alone.

II. Ideological Diversity and Law Schools

Problems with a lack of diversity among faculty and students in law schools have long been analyzed regarding the categories of race and gender.¹² In 2015, the first robust analysis of law school ideological diversity was published in the Harvard Journal of Law & Public Policy (hereinafter “2015 study”).¹³ But even prior to this landmark study, it was already well known that law school professors were disproportionately liberal—both when compared to the overall legal profession and the public at large.¹⁴ A study using 2013 data found that 82% of law school professors were Democrats, while only 11% were Republicans.¹⁵ And even once inside legal academia, conservative law school professors appear to be relegated to topics such as law and economics as opposed to the more prestigious topics, such as constitutional law and federal courts.¹⁶

The 2015 study set out to determine if the great disparity between conservative and liberal law professors was the result of discrimination or if there was a more benign, alternative explanation. The results of the 2015 study strongly point to the former explanation over the latter. It found that conservative law professors are more qualified than their liberal peers. When compared to their liberal counterparts, conservative law professors were 68.2% more likely to be former Supreme Court clerks, 24.1% more likely to have graduated from higher-ranked schools, and 5.4% more likely to have served on law review.¹⁷ Conservative law professors also publish more, which is the most important factor in hiring and promotions.¹⁸ Over the course of ten years, conservative professors publishes four to eight more articles than liberal professors.¹⁹ And the scholarship from conservative law faculty is significantly more likely to be cited to, which is a leading measure of scholarly significance.²⁰ These findings combine to make a strong case that the best explanation for law schools’ ideological inequalities is discrimination and not alternative, benign explanations such as diminished qualifications, abilities, or desire to join academia on the part of conservatives.

As discussed in the original 2020 Article on ideological rankings disparities, a series of internal emails from the Harvard Human Rights Journal that surfaced in 2012 demonstrate how the anti-conservative bias also infects legal scholarship.²¹ The emails document an incident in which the journal editors expressed “major concerns” about how a submitted manuscript was from a conservative author.²² The editors concluded that this was “enough to reject the article.”²³ Such practices likely demonstrate why it is harder for conservatives to obtain faculty positions at law schools, as the ability to publish in top journals is the most significant qualification for aspiring law school professors.²⁴

III. Methodology

The original 2020 research used the overall and peer rankings from 2012 to 2021.²⁵ It measured the disparity between the subjective peer rank and the objective overall rank for the top ten conservative law schools and the top ten liberal law schools.²⁶ The Princeton Review’s ideological rankings were used for this conservative/liberal distinction.²⁷ This resulted in the following law schools used for the 2020 study:

Most Conservative
1. Ave Maria School of Law
2. Brigham Young University J. Reuben Clark Law School
3. Samford University Cumberland School of Law
4. George Mason University School of Law
5. Faulkner University Thomas Goode Jones School of Law
6. University of Alabama School of Law
7. Louisiana State University Paul M. Hebert Law Center
8. Mississippi College School of Law
9. Pepperdine University School of Law
10. University of Idaho College of Law

Most Liberal
1. Northeastern University School of Law
2. American University Washington College of Law
3. University of Pennsylvania Law School
4. University of Oregon School of Law
5. University of Maryland School of Law
6. Brooklyn Law School
7. City University of New York School of Law
8. State University of New York University at Buffalo School of Law
9. University of Colorado School of Law
10. Vermont Law School Law Program

While these are the ten most conservative and ten most liberal law schools, this does not mean that the ten conservative law schools are as far to the right as the ten liberal law schools are to the left. For example, a 2018 study found that of the top fifty law schools, Brigham Young University, Pepperdine, and Alabama were the most ideologically balanced.²⁸ And yet all three of these law schools were on either the 2020 or 2022 list of the ten most conservative law schools.

To accurately measure the deviation between the overall rank and the peer rank, the 2020 study designed the following formula, which was also used in this 2022 update²⁹:

By incorporating both the difference and the percentage change, this formula mitigates the variances that would result from only using one method or the other.³⁰

This updated study added the latest two years of rankings (2022 and 2023), which results in a rankings data set from 2012 to 2023. Also, the updated conservative/liberal law school top ten lists were utilized. The revised lists used for this 2022 update removed Samford and Pepperdine from the conservative top ten and replaced them with Texas Tech and Regent.³¹ For the liberal list, Pennsylvania, Maryland, Brooklyn, State University of New York, and Vermont were replaced with Washington University; George Washington University; University of California, Irvine; University of California, Berkeley; and New York University.³²

IV. Results

A. Original Study

In the original 2020 study, the average peer rank deviation for the conservative schools was −11.82 spots from the overall ranking.³³ The average peer rank deviation for the liberal schools was 9.05 from the overall ranking.³⁴ This resulted in a net difference between conservative and liberal schools of 20.87 spots in the rankings. The odds of randomly selecting two groups of ten that average these two levels of disparity or worse is less than 0.003%, or roughly one in 33,000.³⁵

As explained in the original 2020 research, the difference between the objective peer rankings and the subjective measures in the overall rankings is even more disparate than the −11.82 and 9.05 numbers show.³⁶ This is the result of how the peer rank is also included in the overall rankings formula and is heavily weighted at 25%.³⁷ This functions to significantly mitigate the difference between the peer rankings and the objective factors of the overall rankings.³⁸ When this is accounted for by backing out the peer score from the overall score, an even greater disparity emerges. The conservative penalty goes up to −15.76, and the liberal bonus goes up to 12.07, resulting in a net difference of 27.83 spots in the rankings.³⁹

B. Updated Study

Utilizing the same methodology but with updated rankings and ideological lists, an even greater disparity was found than that from two years prior. The conservative penalty increased to −14.41, and the liberal bonus increased to 9.62. After applying the same correction from the 2020 study to control for how the peer ranking is included in the overall rank, the final result is a −19.21 conservative penalty and a 12.83 liberal bonus. This results in a net 32.04 difference in the peer rankings. The odds of a disparity this great being the result of random chance is about 0.0004%, or roughly one in 250,000.⁴⁰

V. Discussion

The results of this updated research are consistent with the 2020 study that it expands on. It is also consistent with the 2015 study on ideological discrimination in law school faculty hiring. In all three of these studies, the results were extreme in the disparities found, and there is little room for any legitimate non-discriminatory explanation. This section includes potential non-discriminatory explanations for how the disparate result found in this study came about. Such potential explanations include law journal quality, effectiveness of promotional materials, faculty quality, willingness to game the system, and how there are more elite law schools in the liberal group. Unfortunately, none of these explanations are a reasonable candidate to explain any disparity, much less the extreme thirty-two-place difference in the rankings. This section also provides a discussion regarding why the rankings disparity is increasing, the harms of a lack of ideological diversity, and an analogy to an unrelated employment discrimination hypothetical to demonstrate the severity and obviousness of ideological discrimination in legal academia.

A. Law Journal Quality

There is some evidence to suggest that a law school’s flagship law journal may affect its peer ranking while not directly contributing to the overall ranking.⁴¹ Flagship law journal prestige is a convenient proxy for deans who may not have the time or inclination to analyze the nuanced aspects of the 189 law schools they are tasked with ranking.⁴² Indeed, there does exist a high correlation between a law school’s flagship journal’s rankings in the Washington & Lee Law Journal Rankings and its peer rank.⁴³ The ranking of a law school’s flagship journal is also an effective predictor of the law school’s future overall ranking.⁴⁴

Analyzing the flagship law journals from the twenty law schools used in this 2022 study finds no evidence to support the claim that any of the peer rankings disparity is attributable to deviations in law journal quality relative to the overall ranking of the law school. When comparing the overall ranking of conservative and liberal law schools to the quality of their flagship law journals, the results are nearly identical.⁴⁵ The average ratio of overall ranking to impact factor for the conservative law schools is 199.6, while the liberal law schools average 195.0. And when comparing the average ratio of overall ranking to the Washington & Lee Law Journal Rankings Combined Score, the conservative law schools average 8.54, while the liberal law schools average 8.63.

B. Promotional Materials

Due to the importance of the overall rankings, and how the peer rankings contribute significantly to the overall rankings, some law schools distribute promotional materials to deans in an effort to improve their peer rankings.⁴⁶ Therefore, part of the disparity uncovered in this study could be accounted for if the liberal schools engaged in this practice while the conservative schools did not. It is beyond the scope of this research to investigate the extent to which each of the twenty law schools in this study engage in sending out promotional materials. But it is highly unlikely that there would be a significant difference in this area since all law schools have the same incentive to engage in the practice. Furthermore, the effect of these promotional materials is likely minimal and, as some have suggested, possibly even non-existent.⁴⁷ Therefore, this is unlikely to explain any significant portion of the extreme disparity found in this study.

C. Faculty Quality

Despite U.S. News & World Report’s plans to implement quality of faculty scholarship as a factor in the overall rankings prior to the COVID-19 pandemic, the overall rankings still do not take this factor into account.⁴⁸ Regardless, increasing the quality of faculty scholarship would likely result in improved peer rankings but would have no impact on the overall rankings beyond the 25% weighting of the peer score in the overall rankings. Therefore, high-quality faculty scholarship could potentially explain a positive disparity between peer rankings and overall rankings. However, the data reveal that this potential explanation is counterproductive, as faculty scholarship makes the peer rankings disparities found in this study even less likely, not more likely. This is because, as mentioned from the 2015 study, conservative law professors are disproportionately better scholars, not worse.⁴⁹

D. Willingness to Game the System

One could theorize that it is not the peer rankings that are unjustifiably low for the conservative law schools and high for the liberal law schools but rather that the peer rankings are accurate and that it is the overall rankings that are unjustifiably high for the conservative law schools and low for the liberal schools. This would be highly unlikely given that the overall score is primarily the result of objective measures—and is, therefore, neither artificially high nor low but exactly what the objective measures produce. However, law schools sometime attempt to game the system to make these objective measures better than would otherwise be the case. The following are all examples of what law schools have done in an effort to improve their overall rankings:

Pay underperforming graduates to not take the July bar exam, which results in an increased bar passage rate⁵⁰
Temporarily hire unemployable graduates to increase the employment rate of graduating students⁵¹
Prefer potential students with high undergraduate GPAs from mediocre colleges as opposed to potential students with mediocre GPAs from exceptional undergraduate colleges in order to improve the law school’s selectivity score⁵²
Pay the larger university directly for the law school’s electricity expenditures from tuition dollars instead of having it deducted directly from the tuition, thus increasing the reported per-student financial outlays of the law school⁵³
Blatantly falsify GPA and LSAT scores from entering students to increase the selectivity score⁵⁴

If a significant number of law schools engaged in these practices, then law schools who did not would have artificially lower overall rankings by comparison. Therefore, if the conservative law schools used in this study did not engage in these practices, but most other law schools did, this would provide a non-discriminatory reason why their peer ranks are less than their overall ranks.

While this objection is logically sound, it would be difficult to prove, as law schools are unlikely to go public with their involvement in such practices. Therefore, this alternative explanation for the peer rankings disparity found in this study is largely unprovable. Regardless, there is no reason to believe that liberal law schools are more likely to engage in these practices that game the system than conservative law schools. And even if this unlikely circumstance were true, the first four practices listed above would not come close to explaining the thirty-two-place difference between peer rankings and overall rankings. The fifth practice mentioned above—that of blatantly falsifying data—could result in extreme disparities, but it would be all but impossible for multiple law schools to get away with falsifying the data to such an extreme extent over the course of a twelve-year period.

E. More Elite Law Schools in the Liberal Group

In considering the law schools that make up the ten most conservative and the ten most liberal lists, it quickly becomes apparent that the liberal list is, on average, higher in both the overall and peer rankings. For the updated 2022 top-ten lists, there are five liberal law schools in the top twenty-five overall and peer rankings and none from the conservative list in the top twenty-five. But this is in no way a benign explanation for the disparities found in this research. This is because the ultimate starting point of a law school’s overall rank is irrelevant when measuring how its peer rank deviates from this starting point. What matters is the upward or downward deviation in the peer rankings from the overall rankings.

As demonstrated, the potential non-discriminatory explanations are ineffective at explaining the existence of a thirty-two-place disparity between conservative and liberal law schools. Furthermore, any other attempt to provide a non-discriminatory explanation faces a daunting uphill battle. This is because any such explanation would have to overcome the strong evidence against anti-conservative bias in other areas of legal academia, as shown in the 2015 study.⁵⁵

F. Interpretations of the Increased Disparity Finding

The result of this updated study not only demonstrates that the extreme disparity between conservative and liberal law schools remains in existence but also demonstrates that the disparity has increased in recent years. While ultimately unquantifiable, it is interesting to consider possible explanations for this trend. This is likely the result of the simplest explanation, that political polarization is increasing in America.⁵⁶ This consistent trend may have experienced a steep increase since the 2020 study was conducted, as views on the COVID-19 pandemic were highly politically polarizing.⁵⁷ A number of other recent events demonstrate increased polarization. The events on January 6, 2021, at the Capitol was a stark demonstration of increased polarization. Harvard Law School students responded by acquiring more than 200 signatures calling for a ban on hiring former Trump administration officials.⁵⁸ And while the Black Lives Matter movement was established in 2013, the movement gained media attention starting in 2020 with controversial protests.⁵⁹

This updated study understates the magnitude of recent increases in ideological discrimination in peer rankings. This is because this updated study does not begin where the 2020 study left off. Instead, it uses the same starting point of the 2020 study, which is the 2012 rankings that came out in 2011. Therefore, the increase in ideological discrimination attributable to recent increases in political polarization are greatly mitigated by the breadth of the time period used. The last two years make up only 17% of the data used to calculate the average disparity. While the findings of this study regarding ideologically discriminatory rankings are conclusive, the cause-and-effect relationship between a general increase in political polarization in society and anti-conservative bias in peer rankings is more speculative.

G. Harm from Lack of Ideological Diversity

The original 2020 study discussed how punishing and rewarding law schools in the rankings for their political ideologies likely perpetuates discrimination against conservative law professors.⁶⁰ But this problem affects far more than just aspiring conservative law professors. The negative externalities of such ideological discrimination also infect legal education and the practice of law.⁶¹ And this type of systemic harm is naturally perpetuated because “teachers tend to recreate the system they know best—the one that produced them.”⁶²

Lack of ideological diversity in the law school classroom and in legal scholarship functions to provide an inferior legal education.⁶³ Six circuit courts have a majority Republican-appointed judges.⁶⁴ A majority of district courts have either a majority of Republican-appointed judges or an equal number of Democrat-appointed and Republican-appointed judges.⁶⁵ Even after President Biden’s first Supreme Court appointment, there will still be a majority of Republican-appointed justices.⁶⁶ Therefore, law students pay a high price for not being exposed to conservative thought. And liberal students are likely harmed to an even greater extent than conservative students since conservative students are more likely to seek out conservative legal voices outside of the classroom.⁶⁷ This is of paramount importance, as understanding the best arguments from the conservative side will better equip liberals to argue for liberal causes.⁶⁸ Finally, only being exposed to one side of nuanced issues hinders the ability of liberal students to modify their positions in light of a fair assessment of the strongest arguments from both sides.

For this same reason, ideological discrimination is harmful to society at large because people who hire lawyers may rely on the overall rankings of their law schools as a proxy for lawyer quality.⁶⁹ Therefore, an ideologically discriminatory factor in the overall rankings is counterproductive because most people would likely prefer to hire a lawyer who is familiar with—and therefore better equipped to address—conservative arguments and conservative judges. In this way, including peer assessment scores in the overall rankings contributes to hiring-market inefficiencies.

H. Employment Discrimination Analogy

To demonstrate the extreme nature of the disparities uncovered in this research, imagine the following hypothetical analogy of employment discrimination: A business with 189 employees gives raises and promotions based on a combination of an overall score from objective employee performance and a subjective ranking score. Research finds that for the ten most devout Muslim employees, their subjective ranking is, on average, nineteen spots below where one would expect it to be based on their objective employee performance. Furthermore, for the ten most Christian employees, their subjective ranking is thirteen spots above where one would expect it to be based on their objective employee performance. Further imagine that while the employees who make up these two top-ten lists vary from year to year, the drastic disparity against Muslims and for Christians remains the same. And finally, imagine that emails surface demonstrating that when the Muslim employees attempt to conduct the training that is required for promotions, they are sometimes prevented from doing so based on their religion.

A hypothetical person being made aware of such extreme evidence of discrimination would have no reasonable choice but to acknowledge its existence. Such a person is, of course, free to hope and wish against the odds for some as-of-yet unknown, benign explanation of the evidence to emerge. But until such an explanation comes along, the conclusion that pervasive discrimination is involved is the only logical conclusion. Likewise, the evidence for ideological discrimination in legal academia is more than enough to overwhelm even the most skeptical observer who honestly considers the evidence. Such a person is free to hope and wish for some as-of-yet unknown, benign explanation to emerge. But until it does, the conclusion that pervasive discrimination is involved is the only logical conclusion.

VI. Proposed Solution

If there is a silver lining to be found in this research, it is the existence of a simple and effective solution. Peer review scores should be excluded as a factor in the overall rankings. Even disregarding their discriminatory effect, peer rankings as a factor in the overall rankings makes little sense. Most prospective law students likely care far more about small class sizes, minimizing debt, campus amenities, passing the bar, and acquiring a job upon graduation. Therefore, student-faculty ratio, average student debt, per-pupil spending, bar passage rates, and employment rates should be emphasized in the overall rankings. While all of these factors are currently present in the overall rankings formula, the peer assessment score is weighted more than any of them.⁷⁰ Furthermore, there already exists a lawyers and judges assessment score that contributes to the overall score.⁷¹ The average law student likely finds this metric far more significant, as lawyers and judges hire many more law school graduates than do law school deans.

When the lack of ideological diversity is understood, it becomes highly peculiar how little law schools devote to the topic when compared to other categories of inequalities, such as racial imbalances. After all, focusing on the race of faculty and students is a rather circuitous method of achieving increasing diversity of opinion. Even worse, using race as a proxy for increasing diversity of opinion perpetuates harmful stereotypes. This is because implicit in the logic that increasing minority professors will increase diversity of ideas is the belief that different races necessarily think differently, which is at the heart of much white supremacist advocacy.⁷²

Even if the issue of ideological discrimination were absent, there is still good reason to remove peer rankings as a factor in the overall rankings. This is because the peer assessment rankings are affected by the objective factors already measured in the overall rankings. If a law school significantly improves its former student bar passage rates and entering student credentials, it is more likely to receive improved rankings from voting deans.⁷³ Therefore, the peer assessment score and the other factors, such as the bar passage rate and entering student LSAT scores, are collinear terms. In statistics, it is prudent to remove at least one of two collinear terms.⁷⁴ And here, it is clearly the best practice to remove the one that is subjective and perpetrates harmful discrimination.⁷⁵ One final benefit to using objective factors instead of peer rankings in the overall rankings is that peer rankings are a lagging indicator.⁷⁶ Changes in objective factors, such as entering LSAT scores, immediately impact the rankings, while peer rankings are far less responsive.⁷⁷

Conclusion

This Article produces a strong, cumulative case for the existence of ideological discrimination in law school in general and, more specifically, in the peer rankings. The conclusion of ideological discrimination is further strengthened when the results of this research are considered in tandem with the compelling evidence of ideological discrimination in hiring law professors⁷⁸—a decision in which law school deans also play a significant role. Because peer rankings are the leading factor in the overall rankings, this anti-conservative bias also inflicts a conservative penalty there as well, although less severe.

While the magnitude of ideological bias discovered in this study may be surprising, the notion that law school deans—consciously or otherwise—apply a conservative penalty and liberal bonus when ranking law schools is not surprising. The political ideologies of law school deans are likely comparable with those of law school faculty—which are highly disproportionately liberal.⁷⁹ Political ideology is a significant factor that affects how people interpret information.⁸⁰ Just as a conservative may view a liberal law school with heightened skepticism, it appears liberal law school deans view conservative law schools in this same way. Recent polarizing events such as the January 6 Capitol insurrection, COVID-19, and the Black Lives Matter movement are ideal candidates for why this ideological bias has increased since the 2020 study.

This Article documents the harm to conservative professors, law students, and society at large from ideological bias in law school rankings. Fortunately, there is a simple solution to the problem. Removing the peer score from the overall rankings calculation will reduce such harm while providing the benefits of better informing prospective law students, reducing inefficiencies in the hiring market, and contributing to greater ideological diversity in law schools and legal scholarship.

* Powell Endowed Professor of Business Law, Angelo State University. ↩︎
Michael Conklin, Political Ideology and Law School Rankings: Measuring the Conservative Penalty and Liberal Bonus, 2020 U. Ill. L. Rev. Online 178 (2020), https://www.illinoislawreview.org/wp-content/uploads/2020/08/Conklin.pdf. ↩︎
Id. at 183. ↩︎
Robert Morse et al., Methodology: 2023 Best Law Schools Rankings, U.S. News & World Rep. (Mar. 28, 2022, 9:00 PM), https://www.usnews.com/education/best-graduate-schools/articles/law-schools-methodology. ↩︎
Jeffrey Harmatz, US News & World Report Law School Rankings: A Double-Edged Sword?, L. Crossing (Mar. 19, 2013), https://www.lawcrossing.com/article/900012518/US-News-World-Report-Law-School-Rankings-A-Double-Edged-Sword/ (last visited July 6, 2020) (“Regardless of its flaws, US News & World Report’s Top Law School rankings are the most popular and preferred law school rankings in the nation, and have become a legal industry institution.”). ↩︎
Jeffrey Evans Stake, The Interplay Between Law School Rankings, Reputations, and Resource Allocation: Ways Rankings Mislead, 81 Ind. L.J. 229 (2006). Because of the role undergraduate GPA plays in the overall rankings, schools favor applicants from mediocre colleges with high GPAs over applicants from elite college with mediocre GPAs. Id. at 232. Likely in an effort to affect peer rankings, law schools spend “substantial sums” of money on promotional materials to send to other legal academics. Id. at 240. Law schools can increase their standing through accounting tricks, such as paying the greater university directly for their electricity expenditures from tuition dollars instead of having it deducted from the tuition. Id. at 241. While this produces no net difference, it increases the financial outlay on resources, which is a factor in the overall rankings. Id. ↩︎
Katherine Mangan, Villanova U. Reveals Its Law School Gave False Reports of GPA’s and Test Scores, Chron. Higher Ed. (Feb. 6, 2011), https://www.chronicle.com/article/Villanova-U-Reveals-Its-Law/126286; Mark Hansen, U of Illinois Law School Admits to Six Years of False LSAT/GPA Data, A.B.A. J. (Nov. 8, 2011, 12:21 AM), https://www.abajournal.com/news/article/illinois_law_admits_to_six_years_of_false_lsat_gpa_data. ↩︎
Benjamin H. Barton, Fixing Law Schools: From Collapse to the Trump Bump and Beyond 151 (2019) (explaining that InfiLaw—the owner of for-profit Arizona Summit Law school, Florida Coastal School of Law, and recently closed Charlotte School of Law—pays underperforming students not to take the July bar exam after graduating). ↩︎
Elie Mystal, Some Students Want Their Deans Fired After Poor Showing in the U.S. News Rankings (and One Head That’s Already Rolled), Above the L. (Mar. 14, 2013, 11:20 AM), https://abovethelaw.com/2013/03/some-students-want-their-deans-fired-after-poor-showing-in-the-u-s-news-rankings-and-one-head-thats-already-rolled/ (“Ever year, deans and assistant deans find themselves ‘pushed out’ of a job thanks to the U.S. News rankings.”). ↩︎
Robert C. Bird, Advice for the New Legal Studies Professor, 29 J. Legal Stud. Educ. 239, 251 (2012) (“The quality of a law review is roughly determined by the prestige of the law school in which the journal is housed.”). ↩︎
Morse et al., supra note 3. ↩︎
James C. Phillips, Why Are There So Few Conservatives and Libertarians in Legal Academia? An Empirical Exploration of Three Hypotheses, 39 Harv. J.L. & Pub. Pol’y 153, 158 (2015). ↩︎
Id. ↩︎
Adam Bonica et al., The Legal Academy’s Ideological Uniformity, 47 J. Legal Stud. 1 (2018) (“We find that 15 percent of law professors, compared with 35 percent of lawyers, are conservative. This may not simply be due to differences in their backgrounds: the legal academy is still 11 percentage points more liberal than the legal profession after controlling for several relevant individual characteristics.”). ↩︎
James Lindgren, Measuring Diversity: Law Faculties in 1997 and 2013, 39 Harv. J. L. & Pub. Pol’y 89 (2016). ↩︎
Phillips, supra note 11, at 162–63. ↩︎
Id. at 183. ↩︎
Id. at 166. ↩︎
Id. at 195. ↩︎
Id. at 166. ↩︎
Conklin, supra note 1, at 180. ↩︎
Paul Caron, The Secret Sauce for Law Review Placement: Letterhead, Citations, and Liberal, TaxProf Blog (Sept. 13, 2012), https://taxprof.typepad.com/taxprof_blog/2012/09/the-secret.html. It is important to note that although the Harvard Human Rights Journal deemed this author “incredibly conservative,” this assessment was based on his record of government service of clerking for a conservative judge and working at the White House under the Bush administration. Id. He also participated in public debate, at least one time writing something critical of a liberal Supreme Court justice. Id. ↩︎
Id. The state of ideological bias in legal academia is likely also on display in how this event was described. The clear implications were downplayed as something that merely “suggest[s] possible bias,” id., and “possible evidence of bias against conservatives,” David Lat, A Look Inside the Law Review Sausage Factory—and Possible Evidence of Bias Against Conservatives, Above the L. (Sep. 13, 2012, 12:20 PM), https://abovethelaw.com/2012/09/a-look-inside-the-law-review-sausage-factory-and-possible-evidence-of-bias-against-conservatives/. Some even suggested that it is a defense to such discriminatory behavior that “Hey, we’ve seen far worse emails coming out of [Harvard Law School]!” Id. ↩︎
LawProfBlawg, Why do Law Professors Write Law Review Articles?, Above the L. (May 9, 2017, 2:00 PM), https://abovethelaw.com/2017/05/why-do-law-professors-write-law-review-articles/. ↩︎
Note that the 2021 rankings are published in 2020 and based on 2019 data. Christopher J. Ryan, Jr., Of Law School Rankings, Disparity, and Football, 110 Geo. L. J. Online 19 (2021). ↩︎
Conklin, supra note 1, at 181. ↩︎
Most Conservative Students, Princeton Rev., https://www.princetonreview.com/law-school-rankings?rankings=most-conservative-students (last visited April 4, 2022); Most Liberal Students, Princeton Rev., https://www.princetonreview.com/law-school-rankings?rankings=most-liberal-students (last visited April 5, 2022). ↩︎
Bonica et al., supra note 13, at 14. ↩︎
Conklin, supra note 1, at 182. ↩︎
Id. at 181 n.21. ↩︎
Most Conservative Students, supra note 26. ↩︎
Most Liberal Students, supra note 26. ↩︎
Conklin, supra note 1, at 183. ↩︎
Id. ↩︎
Id. Note that the 2020 research only calculated the probability of achieving a score of −11.82 or less for one group of ten randomly selected law schools. It did not also factor in the odds of concurrently randomly selecting a second group of ten averaging 9.04 or more. Based on the same computer simulation with 100,000 occurrences, this produces the two probabilities of 0.0008 and 0.03119, respectively. Applying the multiplication rule probability, the odds of both of these occurring in the same sample is 0.00002495, or 0.002495%. ↩︎
Id. ↩︎
Morse et al., supra note 3. ↩︎
U.S. News & World Report does not provide the specific scores for each factor that makes up the overall ranking of a given law school. However, since the weight of the peer assessment score in the overall rankings is known (25%), the effect of removing it from consideration can be calculated by simply multiplying the difference between the overall rankings and the peer rankings by a factor of 1.33. ↩︎
Conklin, supra note 1, at 183. ↩︎
This is based on the same computer simulation from the 2020 study. It calculated 100,000 randomly selected groups of ten. Only fourteen were equal to or less than −14.41, and 289 were equal to or greater than 100,000. Applying the multiplication rule probability this results in 0.00014 × 0.0289, which equals 0.000004, or 0.0004%. ↩︎
Alfred L. Brophy, The Relationship Between Law Review Citations and Law School Rankings, 39 Conn. L. Rev. 43, 55 (2006) (“The findings suggest that law reviews are schools’ ambassadors to the rest of the legal academy. Much of what people at other schools know about a school’s academic orientation may come from the articles and notes published in the school’s law journals.”). ↩︎
Id. ↩︎
Id. at 48. ↩︎
Alfred L. Brophy, The Emerging Importance of Law Review Rankings for Law School Rankings, 2003-2007, 78 Univ. Colo. L. Rev. 35, 35 (2007) (“Thus, . . . if one wants to know where a law school is heading, . . . one should spend some time studying the scholarship its primary law review publishes.”). ↩︎
To properly perform such an analysis, one must first formulate the ratio so that the two variables are positively related, instead of inversely related. This is accomplished by simply subtracting the law school’s overall ranking by 190 (total number of law schools plus one) and then multiplying by −1. This converts the ranking so that the higher the number, the better the law school. This is necessary to compare to the flagship journal’s impact factor and combined score, for which a higher number indicates a higher quality journal. ↩︎
Stake, supra note 5, at 240. ↩︎
Andrew P. Morriss, Legal Education Through the Blurry Lens of US News Law School Rankings, 20 Green Bag 2d. 253, 257 (2017). ↩︎
US News & World Reports Scholarly Impact Project, Hein Online, https://help.heinonline.org/kb/us-news-world-reports-scholarly-impact-project/ (last visited April 3, 2022). ↩︎
Phillips, supra note 11, at 195–201. (Conservatives publish at significantly higher rates and their research is cited to at significantly higher rates.). ↩︎
Barton, supra note 7, at 151. ↩︎
David Lat, In Defense of Law Schools Hiring Their Own Graduates, Above the Law (Mar. 28, 2013, 6:06 PM), https://abovethelaw.com/2013/03/in-defense-of-law-schools-hiring-their-own-graduates/. ↩︎
Stake, supra note 5, at 232. ↩︎
Id. at 241. ↩︎
Mangan, supra note 6. ↩︎
See Phillips, supra note 11. ↩︎
Levi Boxell, Matthew Gentzkow & Jesse M. Shapiro, Cross-Country Trends in Affective Polarization (Nat’l Bureau of Econ. Rsch., Paper No. 26669, 2021), https://www.nber.org/system/files/working_papers/w26669/w26669.pdf. ↩︎
See, e.g., Thomas B. Edsall, America Has Split, and It’s Now in ‘Very Dangerous Territory’, N.Y. Times (Jan. 26, 2022), https://www.nytimes.com/2022/01/26/opinion/covid-biden-trump-polarization.html. ↩︎
Emmy M. Cho, Harvard Law Students Call on School to Refuse to Hire Former Trump Officials, Harv. Crimson (Feb. 17, 2021), https://www.thecrimson.com/article/2021/2/17/his-petition-trump-officials/. ↩︎
Black Lives Matter: A Timeline of the Movement, Cosmopolitan (Apr. 21, 2021, 9:56 AM), https://www.cosmopolitan.com/uk/reports/a32728194/black-lives-matter-timeline-movement/. ↩︎
With the importance of law school rankings, law school deans are heavily incentivized to hire and promote faculty who will help, not hinder, their advancement in the rankings. Under the current rankings system and the severe conservative penalty, this would include discriminating against conservative faculty. ↩︎
See Phillips, supra note 11, at 158. ↩︎
See Jan M. Levine, Voices in the Wilderness: Tenured and Tenure-Track Directors and Teachers in Legal Research and Writing Programs, 45 J. Legal Educ. 530, 541 (1995). ↩︎
See Adam S. Chilton & Eric A. Posner, An Empirical Study of Political Bias in Legal Scholarship, 44 J. Legal Stud., 277 (2015). ↩︎
There are Republican-appointed majorities in the Third, Fifth, Sixth, Seventh, and Eighth Circuit Courts of Appeals. See Current Federal Judges by Appointing President and Circuit, Ballotpedia, https://ballotpedia.org/Current_federal_judges_by_appointing_president_and circuit (last visited Apr. 10, 2022). ↩︎
There are thirty-five Republican-appointed majorities, sixteen tied Republican-Democrat-appointed judges, and forty Democrat-appointed majorities on district courts. See id. ↩︎
Currently, Alito, Roberts, Thomas, Gorsuch, Kavanaugh, and Barrett are Republican-appointed, and Sotomayor, Kagan, and Breyer are Democrat-appointed. When Breyer leaves the bench at the end of the 2021–2022 term and is replaced by Ketanji Brown Jackson, the number of Republican-appointed and Democratic-appointed Justices will remain the same. ↩︎
See Jeremy A. Frimer, Linda J. Skitkab & Matt Motylb, Liberals and Conservatives Are Similarly Motivated to Avoid Exposure to One Another’s Opinions, 72 J. Experimental Soc. Psych. 1 (2017). ↩︎
See Roger Clegg, Toward Intellectual Diversity in Law School, Minding the Campus (Nov. 7, 2014), https://www.mindingthecampus.org/2014/11/07/toward-intellectual-diversity-in-law-school/. ↩︎
See Richard E. Redding, “Where Did You Go to Law School?” Gatekeeping for the Professoriate and Its Implications for Legal Education, 53 J. Legal Educ. 594, 596 (2003). ↩︎
For example, the bar passage rate is weighted only 3%, while the peer assessment score is weighted 25%. See Morse et al., supra note 3. ↩︎
See id. ↩︎
See Michael E. Ruane, A Brief History of the Enduring Phony Theories That Perpetuates White Supremacy, Wash. Post (Apr. 30, 2019, 11:38 AM), https://www.washingtonpost.com/local/a-brief-history-of-the-enduring-phony-science-that-perpetuates-white-supremacy/2019/04/29/20e6aef0-5aeb-11e9-a00e-050dc7b82693_story.html. ↩︎
Although, these factors do affect the peer rankings on a delayed timeframe. See Christopher J. Ryan, Jr. & Brian L. Frye, A Revealed-Preferences Ranking of Law Schools, 69 Ala. L. Rev. 495, 500 (2017). ↩︎
Ryan, supra note 24, at 25–26. ↩︎
See id. ↩︎
See Ryan & Frye, supra note 72, at 506. ↩︎
See id. at 503. ↩︎
See Phillips, supra note 11. ↩︎
See generally Bonica, supra note 13. ↩︎
See, e.g., Jennifer Jerit & Jason Barabas, Partisan Perceptual Bias and the Information Environment, 74 J. Pol. 672, 672 (2012) (“[P]eople perceive the world in a manner consistent with their political views. The result is a selective pattern of learning in which partisans have higher levels of knowledge for facts that confirm their world view and lower levels of knowledge for facts that challenge them.”). ↩︎

Category: Articles

Vol. 39, Online Supplement

Articles

Notes

Volume 38, Issue 2: The Dignity of Work

Articles

Note

Vol. 38, Online Supplement

Articles

Notes

An Ecological Approach to Data Governance

I. Introduction

II. (Re)Defining Data

III. Why Data Governance?

IV. An Ecological Approach to Data and Governance

V. Data Ecology in Action

Law School Rankings And Political Ideology: Measuring The Conservative Penalty And Liberal Bonus With Updated 2023 Rankings Data

Introduction

I. Law School Rankings

II. Ideological Diversity and Law Schools

III. Methodology

IV. Results

V. Discussion

VI. Proposed Solution

Conclusion